Cauda EVs miRNAs analysis using data from both runs

Libraries required for data analysis

source("src.R")

Data: read counts table from both runs

counts_total_run3 <- read.table(
  file = "run3_excerpt_counts/exceRpt_miRNA_ReadCounts.txt",
  header = TRUE, sep = "", row.names = 1
)

counts_total_run4 <- read.table(
  file = "run4_excerpt_counts_/exceRpt_miRNA_ReadCounts.txt",
  header = TRUE, sep = "", row.names = 1
)

colnames(counts_total_run4) <- paste0(colnames(counts_total_run4), "_2")

df <- merge(counts_total_run3, counts_total_run4, by = "row.names")
rownames(df) <- df$Row.names
df <- df[, -1]

knitr::kable(head(df))
MS28F1_sample2 MS28F1_sample3 MS28F1_sample4 MS28F1_sample5 MS28F1_sample6 MS28F1_sample7 MS28F1_sample8 MS28F1_sample9 MS28F1_sample11 MS28F1_sample14 MS28F1_sample2_2 MS28F1_sample3_2 MS28F1_sample4_2 MS28F1_sample5_2 MS28F1_sample6_2 MS28F1_sample7_2 MS28F1_sample8_2 MS28F1_sample9_2 MS28F1_sample11_2 MS28F1_sample14_2
mmu-let-7a-1 1.1666667 1.5 1.666667 1.1666667 1.3333333 0.000 0.000 2.000000 7.5833333 4.1666667 2.9166667 1.750 1.000 1.500 0.9166667 0.8333333 1.916667 2 3.333333 4.0000000
mmu-let-7a-1-3p|mmu-let-7c-2-3p 19.0000000 34.0 21.500000 38.0000000 42.0000000 14.000 26.000 42.000000 95.0000000 78.0000000 32.0000000 53.000 31.000 42.000 20.0000000 32.0000000 45.000000 65 103.500000 90.5000000
mmu-let-7a-2 0.6666667 1.0 2.666667 0.6666667 0.3333333 0.000 0.000 3.166667 0.5833333 0.6666667 2.9166667 1.750 0.000 0.500 1.9166667 1.3333333 2.416667 5 2.500000 0.3333333
mmu-let-7a-5p 819.5000000 2422.5 1568.833333 2313.3333333 1867.6666667 1422.833 1469.583 4181.500000 5725.0000000 4915.0000000 3184.1666667 4260.667 2186.333 4359.667 2123.3333333 3710.6666667 4274.666667 5608 7622.750000 7111.8333333
mmu-let-7b 0.0000000 0.0 0.000000 0.0000000 0.0000000 0.000 0.250 0.000000 0.0000000 0.0000000 0.3333333 0.000 0.000 0.000 0.0000000 0.0000000 0.000000 0 0.250000 0.0000000
mmu-let-7b-3p 10.0000000 7.0 9.000000 6.0000000 10.0000000 2.000 4.000 10.000000 18.0000000 23.0000000 10.0000000 7.000 10.000 17.000 0.0000000 7.0000000 9.000000 10 22.000000 22.0000000

Specifying the group & batch while filterByExprs()

counts_total <- df
d <- data.frame(
  row.names = colnames(counts_total),
  group = c(
    "MSUS", "CTRL", "MSUS", "CTRL", "MSUS",
    "CTRL", "MSUS", "CTRL", "CTRL", "MSUS",
    "MSUS", "CTRL", "MSUS", "CTRL", "MSUS",
    "CTRL", "MSUS", "CTRL", "CTRL", "MSUS"
  ),
  batch = c(
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    2, 2, 2, 2, 2, 2, 2, 2, 2, 2
  )
)

counts_total[is.na(counts_total)] <- 0

dds <- calcNormFactors(DGEList(counts_total), method = "TMM")

dds$samples$group <- c(
  "MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL", "CTRL",
  "MSUS", "MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL",
  "CTRL", "MSUS"
)

dds$samples$batch <- c(
  1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
  2, 2, 2, 2, 2, 2, 2, 2, 2, 2
)

mm <- model.matrix(~ batch + group, data = d)
mm
##                   (Intercept) batch groupMSUS
## MS28F1_sample2              1     1         1
## MS28F1_sample3              1     1         0
## MS28F1_sample4              1     1         1
## MS28F1_sample5              1     1         0
## MS28F1_sample6              1     1         1
## MS28F1_sample7              1     1         0
## MS28F1_sample8              1     1         1
## MS28F1_sample9              1     1         0
## MS28F1_sample11             1     1         0
## MS28F1_sample14             1     1         1
## MS28F1_sample2_2            1     2         1
## MS28F1_sample3_2            1     2         0
## MS28F1_sample4_2            1     2         1
## MS28F1_sample5_2            1     2         0
## MS28F1_sample6_2            1     2         1
## MS28F1_sample7_2            1     2         0
## MS28F1_sample8_2            1     2         1
## MS28F1_sample9_2            1     2         0
## MS28F1_sample11_2           1     2         0
## MS28F1_sample14_2           1     2         1
## attr(,"assign")
## [1] 0 1 2
## attr(,"contrasts")
## attr(,"contrasts")$group
## [1] "contr.treatment"
### specifying thebatch while filtering by expression
dds <- dds[which(filterByExpr(dds, design = mm, min.count = 20, min.prop = 0.8)), ]

# normalization by counts per million
en <- log1p(edgeR::cpm(dds))

PCA plots

Samples colored by group and shaped by batched

plgINS::plPCA(en,
  colorBy = dds$samples$group, add.labels = FALSE,
  points.size = 12, shapeBy = paste0("Batch", dds$samples$batch)
)

Samples colored by library sizes and shaped by group

plgINS::plPCA(en,
  colorBy = dds$samples$lib.size, add.labels = FALSE,
  points.size = 12, shapeBy = dds$samples$group
)

Samples colored by library sizes and shaped by batches

plgINS::plPCA(en,
  colorBy = dds$samples$lib.size, add.labels = FALSE,
  points.size = 12, shapeBy = dds$samples$batch
)

Without RUVSeq:

mm <- model.matrix(~ dds$samples$batch + dds$samples$group, data = d)
mm
##                   (Intercept) dds$samples$batch dds$samples$groupMSUS
## MS28F1_sample2              1                 1                     1
## MS28F1_sample3              1                 1                     0
## MS28F1_sample4              1                 1                     1
## MS28F1_sample5              1                 1                     0
## MS28F1_sample6              1                 1                     1
## MS28F1_sample7              1                 1                     0
## MS28F1_sample8              1                 1                     1
## MS28F1_sample9              1                 1                     0
## MS28F1_sample11             1                 1                     0
## MS28F1_sample14             1                 1                     1
## MS28F1_sample2_2            1                 2                     1
## MS28F1_sample3_2            1                 2                     0
## MS28F1_sample4_2            1                 2                     1
## MS28F1_sample5_2            1                 2                     0
## MS28F1_sample6_2            1                 2                     1
## MS28F1_sample7_2            1                 2                     0
## MS28F1_sample8_2            1                 2                     1
## MS28F1_sample9_2            1                 2                     0
## MS28F1_sample11_2           1                 2                     0
## MS28F1_sample14_2           1                 2                     1
## attr(,"assign")
## [1] 0 1 2
## attr(,"contrasts")
## attr(,"contrasts")$`dds$samples$group`
## [1] "contr.treatment"
dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "dds$samples$groupMSUS")
res_without_ruvseq <- as.data.frame(topTags(fit, Inf))
head(res_without_ruvseq, n = 20)
hist(res_without_ruvseq$PValue)

With RUVSeq (Only with 1 SV)

re <- RUVSeq::RUVs(en,
  cIdx = row.names(en), k = 1, scIdx =
    RUVSeq::makeGroups(d$group), isLog = TRUE
)
d$SV1 <- re$W[, 1]
d$SV1
##  [1] 2.259766 2.329333 2.008852 2.075830 2.135949 2.081968 2.001994 2.516279
##  [9] 2.501197 2.496698 2.561585 2.564313 2.319754 2.419298 2.394952 2.324093
## [17] 2.327581 2.732388 2.780277 2.715850
mm <- model.matrix(~ d$SV1 + dds$samples$batch + dds$samples$group, data = d)
mm
##                   (Intercept)    d$SV1 dds$samples$batch dds$samples$groupMSUS
## MS28F1_sample2              1 2.259766                 1                     1
## MS28F1_sample3              1 2.329333                 1                     0
## MS28F1_sample4              1 2.008852                 1                     1
## MS28F1_sample5              1 2.075830                 1                     0
## MS28F1_sample6              1 2.135949                 1                     1
## MS28F1_sample7              1 2.081968                 1                     0
## MS28F1_sample8              1 2.001994                 1                     1
## MS28F1_sample9              1 2.516279                 1                     0
## MS28F1_sample11             1 2.501197                 1                     0
## MS28F1_sample14             1 2.496698                 1                     1
## MS28F1_sample2_2            1 2.561585                 2                     1
## MS28F1_sample3_2            1 2.564313                 2                     0
## MS28F1_sample4_2            1 2.319754                 2                     1
## MS28F1_sample5_2            1 2.419298                 2                     0
## MS28F1_sample6_2            1 2.394952                 2                     1
## MS28F1_sample7_2            1 2.324093                 2                     0
## MS28F1_sample8_2            1 2.327581                 2                     1
## MS28F1_sample9_2            1 2.732388                 2                     0
## MS28F1_sample11_2           1 2.780277                 2                     0
## MS28F1_sample14_2           1 2.715850                 2                     1
## attr(,"assign")
## [1] 0 1 2 3
## attr(,"contrasts")
## attr(,"contrasts")$`dds$samples$group`
## [1] "contr.treatment"
dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "dds$samples$groupMSUS")
res_with_ruvseq1 <- as.data.frame(topTags(fit, Inf))
head(res_with_ruvseq1, n = 20)
hist(res_with_ruvseq1$PValue)

PCA plot with 1 SV

Samples colored by group and shaped by batched

plgINS::plPCA(re$normalizedCounts,
  colorBy = dds$samples$group, add.labels = FALSE,
  points.size = 12, shapeBy = paste0("Batch", dds$samples$batch)
)

Samples colored by library sizes and shaped by group

plgINS::plPCA(re$normalizedCounts,
  colorBy = dds$samples$lib.size, add.labels = FALSE,
  points.size = 12, shapeBy = dds$samples$group
)

Samples colored by library sizes and shaped by batches

plgINS::plPCA(re$normalizedCounts,
  colorBy = dds$samples$lib.size, add.labels = FALSE,
  points.size = 12, shapeBy = dds$samples$batch
)

With RUVSeq (Only with 2 SVs)

re <- RUVSeq::RUVs(en,
  cIdx = row.names(en), k = 2,
  scIdx = RUVSeq::makeGroups(d$group), isLog = TRUE
)
### add the variable (suppose I have only one) to the colData dataframe:
d$SV1 <- re$W[, 1]
d$SV2 <- re$W[, 2]

mm <- model.matrix(~ d$SV1 + d$SV2 + dds$samples$group, data = d)
dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "dds$samples$groupMSUS")
res_with_ruvseq2 <- as.data.frame(topTags(fit, Inf))

res_with_ruvseq2$LR <- rownames(res_with_ruvseq2)

write_csv(res_with_ruvseq2, "./output/res_with_ruvseq2.csv")

head(res_with_ruvseq2, n = 20)
hist(res_with_ruvseq2$PValue)

PCA plot with 2 SV

Samples colored by group and shaped by batched

plgINS::plPCA(re$normalizedCounts,
  colorBy = dds$samples$group, add.labels = FALSE,
  points.size = 12, shapeBy = paste0("Batch", dds$samples$batch)
)

Samples colored by library sizes and shaped by group

plgINS::plPCA(re$normalizedCounts,
  colorBy = dds$samples$lib.size, add.labels = FALSE,
  points.size = 12, shapeBy = dds$samples$group
)

Samples colored by library sizes and shaped by batches

plgINS::plPCA(re$normalizedCounts,
  colorBy = dds$samples$lib.size, add.labels = FALSE,
  points.size = 12, shapeBy = dds$samples$batch
)

Trying Limma on the same

metadata <- read_excel("/Users/alshanbayeva/data_analysis_september/metadata_caudaEVs.xlsx")
# apply duplicateCorrelation is two rounds
(design <- model.matrix(~ batch + group, metadata))
vobj_tmp <- voom(dds, design, plot = TRUE)
dupcor <- duplicateCorrelation(vobj_tmp, design, block = metadata$Individual)

# run voom considering the duplicateCorrelation results
# in order to compute more accurate precision weights
# Otherwise, use the results from the first voom run
vobj <- voom(dds, design,
  plot = TRUE, block = metadata$Individual,
  correlation = dupcor$consensus.correlation
)

# Estimate linear mixed model with a single variance component
# Fit the model for each gene,
dupcor <- duplicateCorrelation(vobj, design, block = metadata$Individual) # here the original vobj_tmp was used instead of vobj

# But this step uses only the genome-wide average for the random effect
fitDupCor <- lmFit(vobj, design, block = metadata$Individual, correlation = dupcor$consensus.correlation)

# Fit Empirical Bayes for moderated t-statistics
fitDupCor <- eBayes(fitDupCor)
dealimma <- topTable(fitDupCor, number = 50)
dealimma

dealimma$P.Value <- rownames(dealimma)
write_csv(dealimma, path = "/Users/alshanbayeva/Desktop/caudaEVs_story_manuscript/deepak/raw_data_excerpt/the_final_code_for_submission/dealimma_2runs_miRNAsonly.csv")
# EnhancedVolcano(dealimma,  lab = rownames(dealimma),  x = 'F',  y = 'adj.P.Val')

References

report::cite_packages(sessionInfo())
##   - Amezquita R, Lun A, Becht E, Carey V, Carpp L, Geistlinger L, Marini F,Rue-Albrecht K, Risso D, Soneson C, Waldron L, Pages H, Smith M, HuberW, Morgan M, Gottardo R, Hicks S (2020). "Orchestrating single-cellanalysis with Bioconductor." _Nature Methods_, *17*, 137-145. <URL:https://www.nature.com/articles/s41592-019-0654-x>.
##   - Ben Bolstad (2021). preprocessCore: A collection of pre-processing functions. R package version 1.52.1. https://github.com/bmbolstad/preprocessCore
##   - Charlotte Soneson, Michael I. Love, Mark D. Robinson (2015): Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research
##   - Constantin Ahlmann-Eltze, Peter Hickey and Hervé Pagès (2021). MatrixGenerics: S4 Generic Summary Statistic Functions that Operate on Matrix-Like Objects. R package version 1.2.1. https://bioconductor.org/packages/MatrixGenerics
##   - C. Sievert. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida, 2020.
##   - Erich Neuwirth (2014). RColorBrewer: ColorBrewer Palettes. R package version 1.1-2. https://CRAN.R-project.org/package=RColorBrewer
##   - Gregory R. Warnes, Ben Bolker, Lodewijk Bonebakker, Robert Gentleman, Wolfgang Huber, Andy Liaw, Thomas Lumley, Martin Maechler, Arni Magnusson, Steffen Moeller, Marc Schwartz and Bill Venables (2020). gplots: Various R Programming Tools for Plotting Data. R package version 3.1.1. https://CRAN.R-project.org/package=gplots
##   - Hadley Wickham (2007). Reshaping Data with the reshape Package. Journal of Statistical Software, 21(12), 1-20. URL http://www.jstatsoft.org/v21/i12/.
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SessionInfo

devtools::session_info() %>%
  details::details()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 4.0.4 (2021-02-15)
 os       Ubuntu 16.04.7 LTS          
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  de_DE.UTF-8                 
 ctype    de_DE.UTF-8                 
 tz       Europe/Zurich               
 date     2021-06-11                  

─ Packages ───────────────────────────────────────────────────────────────────
 ! package                * version  date       lib source        
   annotate                 1.68.0   2020-10-27 [1] Bioconductor  
   AnnotationDbi          * 1.52.0   2020-10-27 [1] Bioconductor  
   AnnotationFilter         1.14.0   2020-10-27 [1] Bioconductor  
   AnnotationHub            2.22.1   2021-04-16 [1] Bioconductor  
   aroma.light              3.20.0   2020-10-27 [1] Bioconductor  
   ash                      1.0-15   2015-09-01 [1] CRAN (R 4.0.4)
   askpass                  1.1      2019-01-13 [1] CRAN (R 4.0.4)
   assertthat               0.2.1    2019-03-21 [1] CRAN (R 4.0.4)
   backports                1.2.1    2020-12-09 [1] CRAN (R 4.0.4)
   bayestestR               0.9.0    2021-04-08 [1] CRAN (R 4.0.4)
   beeswarm                 0.3.1    2021-03-07 [1] CRAN (R 4.0.4)
   Biobase                * 2.50.0   2020-10-27 [1] Bioconductor  
   BiocFileCache            1.14.0   2020-10-27 [1] Bioconductor  
   BiocGenerics           * 0.36.1   2021-04-16 [1] Bioconductor  
   BiocManager              1.30.12  2021-03-28 [1] CRAN (R 4.0.4)
   BiocParallel           * 1.24.1   2020-11-06 [1] Bioconductor  
   BiocVersion              3.12.0   2020-04-27 [1] Bioconductor  
   biomaRt                * 2.46.3   2021-02-09 [1] Bioconductor  
   Biostrings               2.58.0   2020-10-27 [1] Bioconductor  
   bit                      4.0.4    2020-08-04 [1] CRAN (R 4.0.4)
   bit64                    4.0.5    2020-08-30 [1] CRAN (R 4.0.4)
   bitops                   1.0-7    2021-04-24 [1] CRAN (R 4.0.4)
   blob                     1.2.1    2020-01-20 [1] CRAN (R 4.0.4)
   bookdown                 0.22     2021-04-22 [1] CRAN (R 4.0.4)
   boot                     1.3-27   2021-02-12 [1] CRAN (R 4.0.4)
   broom                    0.7.5    2021-02-19 [1] CRAN (R 4.0.4)
   bslib                    0.2.4    2021-01-25 [1] CRAN (R 4.0.4)
   cachem                   1.0.4    2021-02-13 [1] CRAN (R 4.0.4)
   callr                    3.7.0    2021-04-20 [1] CRAN (R 4.0.4)
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[1] /home/ubuntu/R/x86_64-pc-linux-gnu-library/4.0
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library

 P ── Loaded and on-disk path mismatch.